The Tech Trek

What does it mean to find out what your team is actually good at—and how do you use that insight to grow, scale, and lead effectively?

In this episode, Amir sits down with Pallavi Pal, Head of Product at Grata, to unpack the nuanced art of identifying strengths within product teams. From hiring with purpose to fostering technical and soft skills, Pallavi shares how she built her team from the ground up and established a culture of collaboration and excellence. Whether you’re a product leader, aspiring manager, or simply navigating your growth path in tech, this conversation is packed with frameworks and hard-earned lessons.

✨ Key Takeaways

“Good” is personal and team-specific – Recognize where individual team members naturally lean in and where they need support.

Hiring with intention matters – Building a team from scratch allows leaders to define what “good” looks like for each role early on.

Balancing technical and soft skills is crucial – Successful PMs don’t just understand the product—they empathize with users and collaborate effectively.

Path to people management starts with mentorship – Use mentorship as a low-risk way to identify potential managers.

Culture isn’t just top-down – Product teams should reflect company values while fostering technical curiosity and peer collaboration.

Metrics can’t be mandated – Teams need to co-create their North Star metrics and OKRs to stay engaged and aligned.

⏱️ Timestamped Highlights

[00:20] – Introducing Pallavi and the focus on identifying what your team is great at

[02:05] – Observing behaviors to identify strengths and hesitations

[05:22] – Hiring to match specific skill sets across different product functions

[08:20] – The balance between domain knowledge, technical skills, and soft skills

[12:03] – Identifying future people managers within your team

[16:21] – Building a product culture that aligns with company values but has its own identity

[21:06] – How to define and align around standards and metrics in product

[24:21] – How to connect with Pallavi for follow-up questions

💬 Quote of the Episode

“It’s a lot more art than science. Good is seeing where people lean in—what excites them—and building the team to amplify that.”

– Pallavi Pal

In this episode, Amir sits down with Zach Barney, Co-founder and CEO of Mobly, the system of record for event marketers. Zach’s story takes us from his early ambitions of joining the NSA to a career-altering injury, a serendipitous fall into sales, and eventually the founding of Mobly. This episode explores not only the career pivots that led Zach to entrepreneurship, but also the mental, financial, and strategic challenges he faced along the way.

If you’ve ever thought about switching paths or launching your own thing — especially from a non-technical background — Zach’s journey is proof that drive, vision, and grit can get you there.

🔑 Key Takeaways:

Pivot Points Can Define You: A severe knee injury and life changes redirected Zach’s path from NSA hopeful to tech founder.

Sales is Entrepreneurship Training: Zach views sales as the most entrepreneurial job short of being a founder — giving him the skills and mindset for startup life.

Solve Real Problems: Mobly was born from Zach’s own pain points in the field — and customer validation made the case.

Execution Over Everything: Despite the harsh fundraising climate, Mobly thrived by focusing on product and market fit.

Founding Doesn’t Require Code: Zach’s non-technical background didn’t stop him — and his story encourages others in the same boat.

⏱️ Timestamped Highlights:

00:20 – Intro to Zach Barney and Mobly — from spreadsheets to sales tech for event marketers.

01:50 – Zach’s drive to control his financial destiny, inspired by his upbringing as the oldest of 8.

03:23 – The “spy-to-startup” journey: NSA offer, Russian fluency, and a career-altering knee injury.

06:15 – How a devastating injury forced Zach to pivot, finding a sales job that set the foundation for his future.

08:29 – Falling in love with sales: the accidental career path that turned into a calling.

10:20 – Constant learning: how podcasts, books, and early-stage exposure prepared him for founding.

12:07 – Making the leap: risks, fears, and financial tradeoffs of starting Mobly with five kids to support.

14:07 – Co-founder chemistry: 30 years of friendship becomes a business partnership

16:20 – Building the MVP without a CTO and the power of scrappy execution.

17:48 – Navigating the economic downturn and fundraising panic attacks in a tough VC market.

20:12 – Why Zach is bullish on execution over economic prediction — and how Mobly is thriving.

💬 Quote to Share:

“Sales is the most entrepreneurial job you can have without being an entrepreneur.” – Zach Barney

🔗 Connect with Zach:

📱 Find him on LinkedIn (just don’t automate your message — he can sniff it out instantly!)

Join us in this insightful conversation with Eric Valasek as we explore the crucial relationship between CEOs, product teams, and engineering leaders. Eric shares his expertise on managing prioritization, strategic tech debt, and ensuring engineering teams stay focused and insulated amidst business dynamics.

Key Takeaways:

Balance is Crucial: A company's success depends heavily on balancing business goals, product demands, and engineering capabilities.

Strategic Tech Debt: Not all tech debt is harmful. Strategic tech debt can accelerate business growth, but must be managed and planned carefully.

Upskilling for Growth: Investing in your team's skill development can pay long-term dividends, especially when tackling new technology domains.

Transparency vs. Focus: Protecting your team from constant business shifts ("horse trading") is essential to maintain productivity and morale.

Engineering's Voice: In tech-driven companies, the engineering team often carries significant influence. Leaders must balance innovation with practical business outcomes.

Timestamped Highlights:

00:41 - Eric's introduction and overview of engineering-product-business relationships.

01:30 - Balancing the business, product, and engineering "trifecta."

05:01 - Effective strategies for team skill development and training.

07:26 - Adjusting team velocity and maintaining quality during upskilling.

09:44 - Navigating potential dips in quality when adopting new technologies.

11:57 - Strategic considerations when intentionally incurring tech debt.

14:31 - Managing transparency and team insulation from business volatility.

17:40 - The importance and impact of engineering's voice in technology-centric businesses.

Quote:

"You can't have speed and quality with the same size team with new technologies. You need to plan that development cycle carefully—some trade-offs are necessary."

— Eric Valasek, Engineering Leader

Connect with Eric: https://www.linkedin.com/in/evalasek/

In this episode of The Tech Trek, Amir Bormand sits down with Shang Wang, Co-founder and CTO of CentML, to explore the dynamic landscape of open source AI technologies and how enterprises are rapidly adapting to this growing ecosystem. Shang offers expert insights into why open source solutions are becoming essential in AI development, the advantages in security and privacy, and how CentML strategically contributes to this evolution.

🌟 Key Takeaways:

Open Source Dominance in AI: Open-source technologies have become foundational to AI development, promoting innovation, transparency, and faster problem-solving.

Enterprise Adoption Shift: Enterprises are increasingly embracing open source solutions in AI, driven by the need for greater transparency, data privacy, and community-driven innovation.

CentML’s Impact: CentML leverages open source through developing tools and infrastructure that optimize AI model deployment, training, and performance at scale.

Security and Privacy Advantages: Open-source AI solutions provide enterprises with enhanced control over data privacy and security, challenging traditional assumptions that closed-source means more secure.

💬 Notable Quote:

"Open source gives you more control. If there’s a security flaw, you can fix it. If there’s a privacy issue, you can build safeguards. Closed source leaves you hoping nothing goes wrong.” – Shang Wang

⏰ Timestamped Highlights:

00:00: Introduction to Shang Wang and CentML

01:28: Origins of open source AI in academia

03:30: Differences in developing with open vs. closed-source solutions

05:10: Impact of open-source tools on talent development and recruitment

07:16: Predictions on the future of open-source AI

10:05: Deep dive into CentML’s tools and open-source integrations

19:46: Real-world applications of CentML, exemplified through banking

22:57: Addressing misconceptions about open source security

27:42: How to connect with Shang Wang

📞 Connect with Shang Wang:

LinkedIn: https://www.linkedin.com/in/shang-sam-wang-52851489

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In this episode of The Tech Trek, Amir sits down with Andrew Levy, CEO and Co-founder of AirCover.ai, to explore how agentic AI is transforming the sales landscape. Andrew shares how AirCover builds real-time digital assistants that empower sales teams, the role of humans in AI-driven workflows, and how enterprises—both nimble and traditional—are adopting these tools to leap ahead. From change management to trust-building and the rise of “little language models,” this conversation unpacks what it really means to bring AI into the heart of go-to-market strategies.

🔑 Key Takeaways

1. Real-Time AI for Real-World Sales AirCover.ai builds AI agents that operate in real time alongside sales reps, surfacing the right information at the right moment, and helping teams scale more effectively with digital counterparts.

2. Scaling Expertise, Not Replacing Teams Rather than replacing humans, agentic AI amplifies expertise—like turning one sales engineer into six through virtual counterparts, unlocking growth, not cuts.

3. Human-in-the-Loop Is the Bridge Especially in regulated industries, “human-in-the-loop” AI design helps companies automate workflows while maintaining control, transparency, and trust.

4. Model Confidence Matters for Adoption Andrew emphasizes trust-building in AI by surfacing high-confidence data and leveraging behavior signals to continually improve user experience and relevance.

5. Little Language Models Are the Future Expect a shift from massive models to specialized ones—“little language models”—tailored per team or even per individual, making AI more personalized and effective.

⏱️ Timestamped Highlights

00:00 – Meet Andrew Levy

Intro to Andrew and AirCover.ai – building digital agents for live sales calls.

02:21 – The Origin of AirCover

Andrew shares the story behind the idea, influenced by challenges scaling sales enablement at VMware.

06:50 – Spotting the Market Gap

When tech and market timing intersect: how AI-native thinking unlocked new possibilities.

08:53 – Change Management From Day One

Why ease of use and seamless workflow integration were key in early product design.

11:26 – Enterprise AI Adoption Trends

Big companies are leapfrogging past previous tech gaps by going all-in on AI.

13:55 – AI as an Extension, Not a Replacement

How AI fills capability gaps without threatening job loss—and why that’s a key adoption driver.

16:47 – Agentic Workflows in Action

Examples of tasks AI handles autonomously vs. where human oversight is essential.

20:07 – Confidence, Trust, and Adoption

Andrew talks about how AirCover builds trust through transparency, high-confidence responses, and adaptive learning signals.

22:34 – The Shift to Smaller, Smarter Models

A peek into the near future of AI: narrow, task-specific models that are ultra-personalized.

23:24 – Final Thoughts & How to Connect

Andrew’s contact info and closing takeaways from Amir.

💬 Featured Quote

“This isn't about replacing your team with AI—it's about giving them superpowers. Imagine taking your best solution engineer and scaling their expertise across your entire team.”

— Andrew Levy, CEO of AirCover.ai

Guest: Viraj Narayanan, CEO of Cornerstone AI

🔑 Key Takeaways

Healthcare data is messy by default. It's generated by countless sources with different standards—think EMRs, Apple Watches, and pharmacy systems—making research data fragmented and hard to use.

AI can clean up the mess. Cornerstone AI applies automation to standardize and improve the fidelity of clinical research data, significantly cutting down manual effort.

Productivity > Replacement. Rather than replacing jobs, AI is helping PhDs and data scientists focus on higher-value tasks, enabling more research and faster discovery.

Standardization is foundational. Without clean, consistent data, the insights drawn—even with AI—are limited or flawed.

Trust is earned. The biggest mindset shift is seeing your own messy data cleaned instantly by AI, not a polished demo set.

Patients win too. Cleaner, faster data means more reliable research, potentially more personalized medicine, and better access to understandable information.

💬 Quote of the Episode

“We’re going to look back in 10 years and think—‘I can’t believe we had PhDs doing that kind of manual data work.’”

— Viraj Narayanan

⏱ Timestamped Highlights

00:00 – Intro to Viraj and Cornerstone AI: Automating healthcare data quality

01:54 – The "plumbing problem" of healthcare data and what no one thinks about

04:48 – Why AI in healthcare often starts with admin—not research

05:35 – Steph Curry and SNOMED: How basketball shows us the need for standardization

08:58 – Wild West of research data: From 2% lift to 40%+ with AI

11:41 – Why research is built on redundancy and how AI rewires the model

14:43 – Change management: From trust to technical buy-in to leadership alignment

18:42 – Will AI take jobs? No—but it will transform what we do with talent

21:03 – What patients will see: Cleaner, faster, more understandable data

23:49 – Where to reach Viraj and final thoughts

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On this episode of The Tech Trek, we're diving deep into the intersection of engineering, product, and business thinking with Vineet Goel — Co-Founder and Chief Product & Technology Officer at Parafin, a fast-growing fintech startup powering small businesses on platforms like DoorDash, Amazon, and Walmart.

We unpack what it really means to build a company where engineers are product thinkers, why bringing in product managers too early can backfire, and how AI is reshaping what it means to write code — and who’s best positioned to thrive in this new world.

Vineet shares how Parafin scaled with just two PMs to 25 engineers, why every engineer shadows customer support calls, and how GenAI might collapse the wall between product and engineering entirely.

Whether you're an engineer, product leader, founder, or just curious where the future of tech orgs is headed — this conversation is packed with insights you won’t want to miss.

🧠 Key Takeaways

Don’t hire PMs too early. Founders should own product-market fit before bringing on a product leader.

Engineers need a business mindset. At Parafin, engineers are ruthlessly customer-focused — many even shadow support calls.

GenAI will change everything. Writing code is becoming a commodity. Future engineers will need to blend product and technical skills.

The product org evolves with scale. Vineet shares when and why Parafin added a Head of Product, and how it shifted org dynamics.

PMs should create leverage, not just roadmaps. When engineers are stretched thin, PMs help teams stay focused and effective.

⏱️ Timestamped Highlights

00:46 – What is Parafin?

A fintech startup empowering small businesses on platforms like Amazon and DoorDash with embedded financial services.

02:35 – Org Design at Parafin

Why they built a structure that’s neither product- nor engineering-led, but customer-obsessed.

05:09 – 25 Engineers, 2 PMs

How a product-minded engineering culture powers massive output and scale.

06:40 – Customer Empathy as Culture

Engineers shadow support calls—and sometimes ship fixes within the hour.

08:50 – When to Hire a Head of Product

What prompted the shift, and how it solved growing pains around complexity and speed.

11:59 – PMs Create Leverage

Bringing in PMs at the right time accelerates decision-making and keeps engineers focused.

14:28 – Dual Hat of CPTO

How Vineet balances strategy, execution, and organizational leadership.

16:34 – GenAI’s Impact on Engineers

Code is getting commoditized. Engineers must evolve—or risk becoming obsolete.

19:14 – What Happens to Product Roadmaps?

AI will speed up delivery—product teams need to dream further ahead, faster.

21:11 – The ‘Shift Left’ of Engineering

Engineers are moving closer to the business—Vineet predicts a product-tech hybrid role will dominate.

💬 Quote Worth Sharing

“Being product and business minded will become a necessity—not a nice to have. Code is becoming a commodity. The future belongs to those who can build and think.”

— Vineet Goel, CPTO at Parafin

In this episode, Amir sits down with Meg Henry, Head of People & Talent at Companyon Ventures, to unpack a critical—yet often overlooked—aspect of growing technical teams: onboarding.

Engineering leaders spend weeks hiring top talent, only to fumble the first 90 days. Meg shares a tactical, startup-friendly approach to onboarding that actually helps new hires ramp faster, become productive sooner, and stick around longer. If you’ve ever onboarded a dev by tossing them a laptop and saying "Good luck," this one’s for you.

🗝️ Key Takeaways for Tech Leaders:

Weak onboarding kills productivity. Even A+ hires won’t thrive if they don’t know how to succeed.

You’re losing time, not saving it. A 30-minute onboarding plan can prevent months of confusion.

Hybrid makes things harder. Without structure, async teams sink.

Consistency beats chaos. No two roles are the same, but every new hire should feel supported.

AI can help you scale onboarding. Especially when documentation is scattered across Slack, Notion, and Drive.

🕒 Timestamped Highlights:

[00:02:00] Why startups obsess over hiring—but ignore onboarding

[00:04:30] That awkward new hire phase, and how to design around it

[00:05:45] Hybrid onboarding: Why access > answers

[00:07:15] The two onboarding tracks every company needs: company-wide + role-specific

[00:09:30] Founders want plug-and-play hires—but that doesn’t work without a plan

[00:10:45] "Here’s your map": how tech leads can shortcut the ramp-up curve

[00:13:30] Using ChatGPT to build lightweight onboarding flows? Yes, here’s how

[00:15:45] Spotting weak onboarding when you inherit a team

[00:18:15] Customization vs. consistency: how much is too much?

[00:20:00] Time investment: just 2.5 hours over 3 months

💬 Quote of the Episode:

“Before GPS, you wouldn’t invite someone over and just say, ‘Figure out how to get here.’ Even your most autonomous hires need directions.” — Meg Henry

📬 Connect with Meg:

Meg’s helping early-stage B2B startups scale smarter. Connect with her on LinkedIn (Meg Henry, Companyon Ventures) and ask for her free onboarding template—it’s lightweight, practical, and startup-tested.

In this episode, Carlos Peralta returns to The Tech Trek to dive deep into data culture in the wearable tech space, sharing how WHOOP turns petabytes of real-time biometric data into personalized, actionable insights. We explore the technical complexities behind data ingestion, transformation, and delivery, and how the mission-driven nature of WHOOP influences both their engineering decisions and company culture.

🔑 Key Takeaways

Wearable tech = real-time big data: WHOOP processes petabytes of multimodal data from edge devices to deliver insights to users in near real time.

Data must be actionable, not just abundant: It's not about the quantity of data collected, but how that data is translated into meaningful guidance for users.

ML Ops is central to product success: The data and ML infrastructure team plays a critical role in feature development, roadmap planning, and performance optimization.

Mission fuels motivation: WHOOP’s internal culture is deeply driven by its impact on human performance—employees are often users of the product themselves.

Scalability ≠ just growth: Cost-efficiency, forecasting, and cloud infrastructure readiness are vital to scaling responsibly in a global market.

⏱️ Timestamped Highlights

00:00 – Intro to Carlos & the mission behind WHOOP

02:19 – Data culture at WHOOP vs. traditional companies

04:15 – Scale of data in wearables: petabytes, not megabytes

05:52 – Complexity of ingesting, transforming, and delivering personalized data

08:53 – Striking a balance: Real-time feedback vs. cloud cost efficiency

11:14 – Scaling the platform as the member base expands globally

13:43 – Internal motivation and culture driven by positive impact stories

15:56 – Why data teams are involved early in the product roadmap

17:59 – Carlos’ journey from WHOOP user to WHOOP employee

20:40 – How to connect with Carlos + final thoughts

💬 Quote of the Episode

“You can have petabytes of data, but if you can’t make it queriable, understandable, and actionable—it’s just noise.” — Carlos Peralta

In this episode of The Tech Trek, Amir Bormand sits down with Max Mergenthaler-Canseco, CEO and co-founder of Nixla, to explore the nuanced reality behind startup success. A multi-time founder with experience as both CEO and CTO, Max shares hard-earned lessons from his entrepreneurial journey—including why theoretical knowledge often clashes with real-world execution, how to build a resilient startup team, and the underestimated danger of survivorship bias in startup lore.

From balancing optimism with statistical failure rates to knowing when to focus on strengths over weaknesses, Max delivers practical wisdom for anyone navigating the startup grind. Whether you're a first-time founder or on your third venture, this conversation will leave you thinking differently about what it really takes to succeed in tech.

🔑 Key Takeaways

Experience is not a blueprint, it's a lens. Max breaks down how startup learnings aren’t always repeatable but instead shape the founder’s decision-making over time.

Passion is the sustainability engine. You have to love what you're building, not just what the market wants—otherwise, you won’t last through the inevitable startup grind.

Founders vs. early employees. Understanding the difference in motivation and expectations is crucial to building and managing a startup team effectively.

Survivorship bias is everywhere. Max cautions against building a startup playbook based only on outlier success stories.

Know your lane. Instead of leveling up all weaknesses, focus on doubling down where your strengths make the biggest impact.

⏱️ Timestamped Highlights

00:44 – What is Nixla?

Max introduces his company, a time series forecasting and anomaly detection startup with deep roots in research.

01:34 – Serial founder life

Max gives a quick snapshot of his startup journey, from NLP experiments to YC-backed fintech.

03:21 – Startup experience ≠ shortcut to success

Why practical experience matters more than theoretical frameworks, and how each startup is its own universe.

07:59 – Playing the startup game because you love it

Max explains why loving the problem you’re solving is essential for long-term survival and sanity.

10:53 – Hiring the right people early

What Max looks for in early-stage team members—and why founders shouldn't expect employees to grind the same way they do.

13:24 – CEO vs. CTO: Vision vs. Execution

A thoughtful breakdown of the distinct roles and responsibilities between CEO and CTO, especially in early-stage companies.

16:27 – Strengths over Weaknesses

Why Max believes in focusing on what you do well, rather than fixing every flaw.

20:25 – The trap of survivorship bias

A fascinating conversation about how the startup ecosystem overemphasizes success stories and ignores the valuable lessons of failure.

How to reach Max

LinkedIn: https://www.linkedin.com/in/mergenthaler/

💬 Featured Quote

“The only way to keep playing the startup game is to actually enjoy the game.” — Max Mergenthaler-Canseco